In manufacturing and logistics, the conversation about AI almost always starts at the machines: vision systems on the line, predictive maintenance, route optimisation. These are real, but in a company of 10–250 people they are usually not where the fastest measurable gain sits. The measurable gain is in the paperwork around the line.

There is a simple reason for that. A project tied to the production line needs sensors, data collection, integration and months of work, and it carries the risk of interfering with output. Paperwork, by contrast, is already digital, it runs on written rules, and when it breaks, production does not stop. That is why it is the sensible place to start.

Why manufacturing and logistics differ from an office

Three things are different. First, the documents do not come from one place. In a mid-sized plant, the incoming material arrives with a delivery note, a material certificate, an order confirmation and an invoice, from four suppliers in four different formats, some of it scanned from paper. Second, shift work means information is handed over two or three times a day, and it loses something at every handover. Third, most of the know-how is written down but hard to find. Machine manuals, SOPs, old fault reports, all in PDF, somewhere on a network drive.

All three are text processing problems, not engineering problems. That is good news, because text processing is the most mature and the cheapest part of AI today. It needs no new machine and no shutdown.

Four processes where the impact is genuinely measurable

The figures below are typical industry experience ranges, not guaranteed results. Your own numbers can easily differ, which is why measurement always comes before implementation.

Process	Typical time today	After implementation	Time to live
Processing incoming supplier documents	3–5 minutes / document	1–2 minutes / document	3–5 weeks
Shipping paperwork (waybill, packing list)	6–10 minutes / shipment	2–3 minutes / shipment	4–6 weeks
Shift handover and fault log summary	15–25 minutes / shift	5 minutes / shift	2–3 weeks
Internal document search (manuals, SOPs, drawings)	20–40 minutes / person / day	5–10 minutes / person / day	4–8 weeks

Incoming supplier documents

Reading the delivery note and order confirmation, matching them against the purchase order, and listing the discrepancies. This is the classic case where AI does not decide, it only prepares: it fills the fields and flags where quantity or price differs. Approval stays with a person. The gain shows up because purchasing stops searching and starts checking.

Shipping paperwork

Producing waybills, packing lists and accompanying documents from order data, and matching returned documents back to the shipment. Most of the time here does not go on filling forms, it goes on collecting data from three systems. Once that comes together in one place, the filling itself is quick.

Shift handover and fault log

The fastest payback and the least popular project, because it does not sound exciting. End-of-shift notes, downtime records and fault reports become a one-page summary for the next shift supervisor. After two weeks it usually turns out the recurring faults were visible all along, nobody had just read them together.

Internal document search

A search tool that understands your own manuals, SOPs and earlier fault reports, and answers with a citation. This is not a chatbot, it is a search tool: every answer states which page of which document it came from. Without the citation it is unusable, because it cannot be verified.

A worked example: a 60-person plant

Take a manufacturing company with 60 staff, three shifts and 22 working days a month. They start two processes, not four.

• Incoming supplier documents. 900 documents a month, 4 minutes each on average, which is 60 hours. At a 50 per cent saving, 30 hours a month come back.
• Shift handover. 3 handovers a day over 22 days is 66 handovers. The time drops from 20 minutes to 5, so 15 minutes each, 16.5 hours a month in total.

That is roughly 46 hours a month. At an internal hourly cost of 18 €, it comes to about 830 € a month. The one-off implementation cost at this size is typically 4,500–7,000 €, with running costs of 60–150 € a month. Net monthly saving is therefore around 700 €, and payback lands at 7 to 10 months.

This calculation is a frame, not a promise. What matters in it are the two numbers you have to measure yourselves: how many units, and how much time per unit. Everything else follows from those. If you want a quick order-of-magnitude figure, the calculator runs the same logic on your own data.

What not to automate in manufacturing

There are four areas where the cost-benefit ratio is poor, or the legal risk is out of proportion.

1. The final quality decision. AI can flag that a batch is probably defective. Whether it ships to the customer is signed off by a person. If the decision runs without a signature, accountability disappears with it.
2. Safety functions and machine control. Emergency stop, guard logic, interlocks. These are standardised, deterministic systems. A language model does not belong in that chain.
3. Assessment of people. Performance reviews, shift allocation on disciplinary grounds, candidate screening. The EU AI Act treats these uses as high risk, and the cost of compliance is considerably higher than the benefit.
4. One-off, non-repeating work. If a process runs twice a month, maintaining the automation costs more than the work itself. The rule is simple: without at least weekly repetition and at least 20 units, leave it in human hands.

On top of that comes one general limit: wherever the output goes straight to a customer or an authority, there must always be an approval point. Not because AI errs often, but because even a rare error is expensive and hard to explain after the fact.

How to start: the first four weeks

In the first two weeks you build nothing, you measure. Three numbers need writing down: how many units go through the process, how much time goes on each one, and who does it today. Those three numbers set the order, not enthusiasm.

In the third week one process starts, the lowest-risk one. At the end of the fourth week you compare the measured time with the figure from before. If there is no improvement, the project stops rather than expands. This is the step most companies skip, and it is why they cannot say six months later whether it was worth it.

Training matters as much as the tool. If the plant manager and the shift supervisors do not understand what the system sees and what it does not, they will be back in Excel within a month. Article 4 of the EU AI Act also requires adequate AI literacy from staff, and we have written about that separately on the blog.

Our prices are published, so you do not need a round of quote requests to get an order of magnitude: they are in the price list. At the end of an audit you get a priority order, including which process is best left alone this year.